Recommending contacts in a social network

ABSTRACT

A method and system for recommending potential contacts to a target user is provided. A recommendation system identifies users who are related to the target user through no more than a maximum degree of separation. The recommendation system identifies the users by starting with the contacts of the target user and identifying users who are contacts of the target user&#39;s contacts, contacts of those contacts, and so on. The recommendation system then ranks the identified users, who are potential contacts for the target user, based on a likelihood that the target user will want to have a direct relationship with the identified users. The recommendation system then presents to the target user a ranking of the users who have not been filtered out.

BACKGROUND

A social network consists of individuals and their relationships toother individuals. For example, within a company, the employees andtheir relationships to other employees, such as being members of thesame development team or the same management committee, form a socialnetwork. Each of the employees may also have relationships to theirfamily members and other non-family friends. Each relationship within asocial network specifies a direct relationship between two individuals,such as being members of the same team. Individuals may also haveindirect relationships with other individuals. For example, Tom and Marymay not know each other, but both Tom and Mary have a relationship withJim. In such a case, Tom and Mary would have an indirect relationship toeach other through Jim. The distance (number of relationships) betweentwo individuals within a social network is commonly referred to as their“degree of separation.” For example, Tom and Mary would have two degreesof separation. Because social networks can have hundreds or thousands ofindividuals and direct relationships, social networks can be verycomplex. It would be a difficult and time-consuming task to identifyindividuals and all their relationships within a large social network.

Fortunately, the identity of individuals and their relationships withother individuals can be automatically derived from data stored bycomputer systems. Many individuals use their computer systems to storeindications of relationships to other individuals. In particular, manysoftware applications allow a user to explicitly store names of otherswith whom the user has a relationship. (“User” refers to any individualwho has a relationship represented on a computer system.) The names (orsome other identifiers such as electronic mail addresses) of the otherusers are stored in address lists for electronic mail programs, incontact lists for instant messaging programs, in invitation lists forevent organizing programs, and so on. In addition, the names of theother users can be derived from data that is not an explicit list. Forexample, the names of users can be derived from the to, from, and ccfields of electronic mail messages, from meeting entries within acalendar, from letters stored as electronic documents, and so on. Eachof these other users has a relationship, referred to as a directrelationship, with the user regardless of the “closeness” of therelationship. For example, a user may have a relationship with aco-worker and a relationship with a worker at another company that wascc′d on the same electronic mail message. In this example, therelationship with the co-worker may be closer than the relationship withthe worker at the other company. The users with whom a user has arelationship are referred to generally as “contacts” of that user.

In many situations, a user may need to get in touch with their contacts.For example, a user who is a moving to a new job or moving to a newhouse may want to notify their contacts of the move. As another example,when a user is considering moving to a new job or needs advice on aparticular subject, the user may seek advice from their contacts. Theuser may also request a contact to seek advice on that user's behalffrom the contact's contact. Relying on contacts to propagate a requestthrough a social network can be ineffective and even undesirable incertain situations. For example, as the request propagates from contactto contact, important aspects of the request may be omitted or changed,resulting in any response being ineffective. Also, the request maypropagate to a contact even though the original requestor would not wantthat contact to know about the request. For example, an employeerequesting information about job openings at other companies would notwant that request to propagate to a user at the requester's currentcompany.

SUMMARY

A method and system for recommending potential contacts to a target useris provided. A recommendation system identifies users who are related tothe target user through no more than a maximum degree of separation. Therecommendation system identifies the users by starting with the contactsof the target user and identifying users who are contacts of the targetuser's contacts. The recommendation system continues the process ofidentifying contacts of contacts until the maximum degree of separationis reached. The recommendation system then ranks the identified users,who are potential contacts for the target user, based on a likelihoodthat the target user will want to have a direct relationship with theidentified users. The recommendation system may base the likelihood onthe number of contact paths and the length of the contact paths betweenthe target user and an identified user. The recommendation system mayalso filter out identified users who do not satisfy a recommendationcriterion. The recommendation system then presents to the target user aranking of the users who have not been filtered out. The target user canthen decide whether to add the presented users as one of their contacts.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram that illustrates an example social network.

FIG. 2 lists contact paths with a path length of three or less betweenuser 0 and each of the other users.

FIG. 3 is a diagram that illustrates social paths between two users inone embodiment.

FIG. 4 is a block diagram that illustrates components of therecommendation system in one embodiment.

FIG. 5 is a flow diagram that illustrates the processing of therecommend potential contacts component of the recommendation system inone embodiment.

FIG. 6 is a flow diagram that illustrates the processing of the identifypaths to potential contacts component of the recommendation system inone embodiment.

FIG. 7 is a flow diagram that illustrates the processing of the searchfor contact paths component of the recommendation system in oneembodiment.

FIG. 8 is a flow diagram that illustrates the processing of the rankpotential contacts component of the recommendation system in oneembodiment.

FIG. 9 is a flow diagram that illustrates the processing of the filterdirect contacts component of the recommendation system in oneembodiment.

FIG. 10 is a flow diagram that illustrates the processing of the filtermarginal contacts component of the recommendation system in oneembodiment.

FIG. 11 is a flow diagram that illustrates the processing of theidentify social paths component of the recommendation system in oneembodiment.

DETAILED DESCRIPTION

A method and system for recommending potential contacts to a target useris provided. In one embodiment, a recommendation system identifies userswho are related to the target user through no more than a maximum degreeof separation. The recommendation system identifies the users bystarting with the contacts of the target user and identifying users whoare contacts of the target user's contacts. The recommendation systemcontinues the process of identifying contacts of contacts until themaximum degree of separation (e.g., three) is reached. The sequence ofusers from the target user to an identified user is referred to as a“contact path.” For example, if the target user is Aaron, one of Aaron'scontacts is Bill, and one of Bill's contacts is Carlos, then thesequence of Aaron-Bill-Carlos represents a contact path between Aaronand Carlos. That contact path has a path length of two because Aaron andCarlos are separated by two degrees of separation: Aaron's relationshipto Bill and Bill's relationship to Carlos. The maximum degree ofseparation corresponds to a maximum contact path length between twousers for making contact recommendations. The recommendation system thenranks the identified users, who are potential contacts for the targetuser, based on a likelihood that the target user will want to have adirect relationship with the identified users. The recommendation systemmay base the likelihood on the number of contact paths and the length ofthe contact paths between the target user and an identified user. Forexample, if Aaron has only one contact path to Carlos with a path lengthof two, but Aaron has three contact paths to Cindy with two path lengthsof two and one path length of three, then the recommendation systemwould rank Cindy higher than Carlos because of the multiple contactpaths. The recommendation system may also filter out identified userswho do not satisfy a recommendation criterion. One recommendationcriterion may be that the identified user should not already be acontact of the target user—there is no need to recommend a user who isalready a contact. Another recommendation criterion may be that thereshould be multiple contact paths between the target contact and theidentified user. This criterion helps ensure that only users with strongindirect relationships are recommended to the target user. Therecommendation system then presents to the target user a ranking of theusers who have not been filtered out. The target user can then decidewhether to add presented users as one of their contacts. In this way,the recommendation system can allow a target user to establish a directrelationship with users that are currently only indirectly related.

In one embodiment, the recommendation system ranks the identified usersbased on the number of contact paths between the target user and theidentified users and the lengths of those paths. The recommendationsystem may generate a path score for each contact path between thetarget user and an identified user. The path score may be inverselyproportional to the length of the contact path. The recommendationsystem then aggregates the path scores for each contact path into arecommendation score for the identified user. The recommendation systemmay represent the recommendation score by the following equation:

${{Rank}(Z)} = {\sum\limits_{p \in {({X\rightarrow Z})}}\frac{1}{p}}$

where Rank(Z) represents the recommendation score of identified user Z,p represents the contact path from target user X to identified user Z,and |p| represents the path length of contact path p. The recommendationsystem may alternatively use various other metrics for generating a pathscore such as a path score that geometrically or exponentially decreaseswith path length. The recommendation system may not weight each pathscore equally when aggregating the path scores. For example, therecommendation system may give full weight to the path scores for thefirst three contact paths, but may give decreasing weight to path scoresfor additional contact paths.

In one embodiment, the recommendation system identifies “social paths”within a social path length between a pair of users using the contactpaths. To identify social paths between a pair of users, therecommendation system first identifies contact paths from the first userof the pair and contact paths from the second user of the pair. The sumof the maximum contact path length for the first user and the maximumcontact path length for the second user equals the social path length.For example, if the social path length is six, then the maximum contactpath lengths for the first user and the second user may be three. Insuch a case, the first user A may have a contact path of A-C, A-B-C, andA-B-C-D, and the second user G may have contact paths of G-F-C, G-F-E,and G-F-E-D. After the contact paths for the first user and the seconduser are identified, the recommendation system then determines whetherany of the end users at the end of a contact path of the first user arein common with the end users of a contact path of the second user.Continuing with the example, user C and user D are end users who arecommon to contact paths of both the first user and the second user. Therecommendation system may then generate the social paths based on thecommon users. For example, the recommendation system may generate thesocial paths of A-C-F-G (from A-C and G-F-C), A-B-C-F-G (from A-B-C andG-F-C), and A-B-C-D-E-F-G (from A-B-C-D and G-F-E-D). A social path isdifferent from a contact path because the social path may be consideredto be an undirected path, while a contact path may be considered to be adirected path. Thus, although in the social path A-C-F-G there is adirected path from user A to user C and from user G to user C, theremight not be a directed path from user A to user G. In general, if C isin B's contact list, but B is not in C's contact list, it can be assumedthat C knows B so the social path A-B-C-F-G provides a path throughwhich G may be able to contact A. The generating of social paths betweena pair of users using their contact paths significantly avoidsnon-linearly increasing computational complexity that occurs whensearching a social network for increasing degrees of separation.

FIG. 1 is a diagram that illustrates an example social network. Thesocial network 100 includes nodes 0-12 representing users 0-12 anddirected links between the nodes representing direct relationships. Forexample, because node 0 has a directed link to node 1, then user 1 is inthe contact list of user 0. A sequence of nodes connected by directedlinks represents a contact path. For example, the sequence of nodes0-1-2-5 represents a contact path with a path length of three betweenuser 0 and user 5. The sequence of nodes 0-9-10-8-5 represents a contactpath with a path length of four between user 0 and user 5. The sequenceof nodes 0-1-2-5 also indicates that contact paths 0-1 and 0-1-2 alsoexist between user 0 and users 1 and 2. FIG. 2 lists contact paths witha path length of three or less between user 0 and each of the otherusers. In one embodiment, the recommendation system does not allow thesame user to be added twice to a contact path. Alternatively, thepresence of a duplicate user may indicate a strong relationship betweenthe target user and the duplicate user, which can be factored in whenranking users.

FIG. 3 is a diagram that illustrates social paths between two users inone embodiment. The two users, user X and user Y, are represented bynode X and node Y. The nodes 301 represent the contact paths for user Xof a certain maximum contact path length, and the nodes 302 representthe contact paths for user Y of a certain maximum contact path length.The nodes 303 represent the common nodes of the contact paths of user Xand user Y. Each path from user X to user Y formed by considering thelinks to be undirected is a social path.

FIG. 4 is a block diagram that illustrates components of therecommendation system in one embodiment. The recommendation system 430is connected to the user data stores 410 via communications link 420.The user data stores contain information that includes explicit andimplicit contacts of the users. The recommendation system includes agather social network component 431 and a social network store 432. Thegather social network component searches the user data stores forcontacts of the users and stores contact lists for each user in thesocial network store. The recommendation system also includes arecommend potential contacts component 433 and an identify social pathscomponent 434. The recommend potential contacts component invokes anidentify paths to potential contacts component 435, a rank potentialcontacts component 436, a filter direct contacts component 437, and afilter marginal contacts component 438. The identify paths to potentialcontacts component identifies contact paths from a target user byinvoking a search for contact paths component 439. The rank potentialcontacts component ranks potential contacts based on path length of thecontact paths. The filter direct contacts component filters outpotential contacts who are direct contacts of the target user. Thefilter marginal contacts component filters out contacts who only haveone contact path from the target user. The search for contact pathscomponent performs the searching for contact paths from a target user.The identify social paths component invokes the search for contact pathscomponent for a pair of users and then identifies the social paths fromthe contact paths. The recommendation system may also include a contactpath index 440 that maps end users of contact paths to their contactpaths. The index facilitates the identifying social paths from pairs ofcontact paths.

The computing devices on which the recommendation system may beimplemented may include a central processing unit, memory, input devices(e.g., keyboard and pointing devices), output devices (e.g., displaydevices), and storage devices (e.g., disk drives). The memory andstorage devices are computer-readable media that may containinstructions that implement the recommendation system. In addition, thedata structures, message structures, and instructions may be stored ortransmitted via a data transmission medium, such as a signal on acommunications link. Various communications links may be used, such asthe Internet, a local area network, a wide area network, or apoint-to-point dial-up connection.

The recommendation system may be implemented in and used by variousoperating environments that include personal computers, servercomputers, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, programmable consumer electronics, networkPCs, minicomputers, mainframe computers, distributed computingenvironments that include any of the above systems or devices, and thelike.

The recommendation system may be described in the general context ofcomputer-executable instructions, such as program modules, executed byone or more computers or other devices. Generally, program modulesinclude routines, programs, objects, components, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Typically, the functionality of the program modules may becombined or distributed as desired in various embodiments. For example,the recommend potential contacts component and the identify social pathscomponent may be used independently of each other.

FIG. 5 is a flow diagram that illustrates the processing of therecommend potential contacts component of the recommendation system inone embodiment. The component is passed a target user and identifies andpresents to the target user potential contacts that have been ranked. Inblock 501, the component invokes the identify paths to potentialcontacts component to identify contact paths from the target user topotential contacts with a contact path length less than or equal to amaximum contact path length. In block 502, the component invokes thefilter direct contacts component to remove direct contacts of the targetuser from the potential contacts. In block 503, the component invokesthe filter marginal contacts component to remove potential contacts withonly one contact path from the target user. In block 504, the componentinvokes the rank potential contacts component to rank the potentialcontacts identified in the contact paths. In block 505, the componentpresents the potential contacts as ranked to the target user and thencompletes. In certain implementations, the potential contacts may beused in ways other than presenting to the target user. For example, theknowledge of potential contacts of a target user may be important tothird parties such law enforcement officials, philanthropicorganizations, and so on.

FIG. 6 is a flow diagram that illustrates the processing of the identifypaths to potential contacts component of the recommendation system inone embodiment. The component is passed a target user and then invokesthe search for contact paths component. The search for contact pathscomponent is a recursive component that is passed the current path froma target user, the length of the current path, and a user to add to thepath. In block 601, the component initializes the path to empty. Inblock 602, the component sets the initial length of the path to zero. Inblock 603, the component sets a list of paths to empty. In block 604,the component invokes the search for contact paths component to identifythe contact paths from the target user. In block 605, the componentsorts the list of paths by the end user of the paths to facilitateprocessing of the paths based on the end users. The component may alsogenerate an index of end user to contact path. The contact paths arestored in the list of paths.

FIG. 7 is a flow diagram that illustrates the processing of the searchfor contact paths component of the recommendation system in oneembodiment. The component is passed a path, a length of the path, and auser. The component adds the user to the path and recursively invokesthe component for each direct contact of the passed user to extend thepath up to the maximum contact path length. In block 701, the componentincrements the path length. In block 702, the component adds the passeduser to the path. In block 703, the component adds the path to the listof paths. In decision block 704, if the path length is greater than orequal to the maximum contact path length, then the component returns,else the component continues at block 705. In blocks 705-708, thecomponent recursively invokes itself for each direct contact of thepassed user. In block 705, the component selects the next direct contactof the passed user. In decision block 706, if all the direct contacts ofthe passed user have already been selected, then the component returns,else the component continues at block 707. In decision block 707, if theselected contact is already on the path, then the component loops toblock 705 to select the next direct contact of the passed user, else thecomponent continues at block 708. In block 708, the componentrecursively invokes the search for contact paths component passing thepath, the path length, and the selected contact. The component thenloops to block 705 to select the next direct contact of the passed user.

FIG. 8 is a flow diagram that illustrates the processing of the rankpotential contacts component of the recommendation system in oneembodiment. The component generates a recommendation score for each useridentified as a potential contact by aggregating the path scores for theuser. The component initializes the recommendation score to 0 for eachuser. In blocks 801-807, the component loops selecting each contact pathand aggregating a path score for the selected contact path intorecommendation scores for the identified users. In block 801, thecomponent selects the next contact path. In decision block 802, if allthe contact paths have already been selected, then the componentreturns, else the component continues at block 803. In decision block803, if the end user of the contact path has already been processed bythis component, then the component continues at block 806, else thecomponent continues at block 804. In block 804, the component incrementsan index of potential contacts. The index identifies the end user. Inblock 805, the component maps the end user to the index so that the sameindex can be used for that end user and continues at block 807. In block806, the component selects the index for the end user of the selectedcontact path. In block 807, the component aggregates the path score forthe selected contact path into the recommendation score for the end userof the selected contact path. The component then loops to block 801 toselect the next contact path.

FIG. 9 is a flow diagram that illustrates the processing of the filterdirect contacts component of the recommendation system in oneembodiment. The component removes potential contacts who are alreadydirect contacts of the target user. In block 901, the component selectsthe next potential contact. In decision block 902, if all the potentialcontacts have already been selected, then the component returns, elsethe component continues at block 903. In decision block 903, if theselected potential contact is a direct contact of the target user, thenthe component continues at block 904, else the component loops to block901 to select the next potential contact. In block 904, the componentremoves the selected potential contact from the list of potentialcontacts and then loops to block 901 to select the next potentialcontact.

FIG. 10 is a flow diagram that illustrates the processing of the filtermarginal contacts component of the recommendation system in oneembodiment. The component removes potential contacts who do not satisfythe marginal contact criterion. In block 1001, the component selects thenext potential contact. In decision block 1002, if all the potentialcontacts have already been selected, then the component returns, elsethe component continues at block 1003. In block 1003, the componentcounts the number of contact paths to the selected potential contact. Indecision block 1004, if paths to the selected potential contactsatisfies a marginal contact threshold (e.g., the number of contactpaths is greater than one), then the selected potential contactsatisfies the marginal contact criterion and the component loops toblock 1001 to select the next potential contact, else the componentcontinues at block 1005. In block 1005, the component removes theselected potential contact from the list of potential contacts and thenloops to block 1001 to select the next potential contact. One skilled inthe art will appreciate that many different marginal contact thresholdscan be used. For example, the threshold may be that a contact is notmarginal if there is at least one path of length two or two paths oflength three.

FIG. 11 is a flow diagram that illustrates the processing of theidentify social paths component of the recommendation system in oneembodiment. The component is passed a pair of users and identifies thesocial paths between the users based on contact paths from each of theusers. The component may generate a user index for the contact paths ofthe first user and a user index for the contact paths of the second userto facilitate the locating of common users. In block 1101, the componentinvokes the identify paths to potential contacts component to identifythe contact paths from the first user. In block 1102, the componentinvokes the identify paths to potential contacts component to identifythe contact paths from the second user. In blocks 1103-1108, thecomponent loops identifying social paths between the first user and thesecond user based on common potential contacts as indicated by theidentified paths. In block 1103, the component selects the next contactpath of the first user. In decision block 1104, if all the potentialcontacts of the first user have already been selected, then thecomponent returns, else the component continues at block 1105. In block1105, the component selects the end user of the selected contact path.In block 1106, the component selects the next contact path from thesecond user with the same selected end user. In decision block 1107, ifall such contact paths have already been selected, then the componentloops to block 1103 to select the next contact path from the first user,else the component continues at block 1108. In block 1108, the componentcombines the selected contact paths into a social path between the firstuser and the second user and then loops to block 1106 to select the nextcontact path of the second user.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims. Accordingly, the invention isnot limited except as by the appended claims.

1-20. (canceled)
 21. A computer system for recommending potentialcontacts for a target user based on analysis of contact lists of aplurality of users, comprising: a data store containing contact lists ofthe users, including a contact list for the target user; a memorystoring computer-executable instructions of a component that identifies,from the contact lists, social paths from the target user to otherusers, a social path being a combination of a first contact path fromthe target user to a common user and a second contact path from an otheruser to the common user, such that each user on the first contact pathand the second contact path are on the social path, a contact path froma first user to a second user being a directed path of contactsidentified from the contact lists; a component that filters out users onthe identified social paths who do not satisfy a recommendationcriterion; and a component that ranks the users on the identified socialpaths based on path lengths of the social paths; a component thatpresents to the target user an indication of the ranking of thenon-filtered-out users as recommendations for potential contacts; and aprocessor for executing the computer-executable instructions stored inthe memory.
 22. The computer system of claim 21 wherein the componentthat identifies social paths traverses a social network formed by thecontact lists starting at the contact list of the target user and asocial network formed by the contact lists starting at the other users.23. The computer system of claim 22 wherein the component that ranksusers generates a recommendation score for a user by aggregating a pathscore for each social path between the target user to another user. 24.The computer system of claim 23 wherein the path score for a social pathdecreases as the path length of the social path increases.
 25. Thecomputer system of claim 23 wherein contact paths from the target userto the other users do not exist in the contact lists;
 26. The computersystem of claim 21 wherein the recommendation criterion is that arecommended user is not in the contact list of the target user.
 27. Thecomputer system of claim 21 wherein the recommendation criterion is thatthere are multiple social paths between the target user and the user.28. A computer system for recommending potential contacts to a firstuser, comprising: a data store containing contact lists of users,including a contact list for the first user and a contact list for asecond user; a memory for storing computer-executable instructions of acomponent that identifies, from the contact lists, contact paths fromthe first user to other users and contact paths from the second user toother users; a component that, when there is a contact path from thefirst user to another user and a contact path from the second user tothat same other user, indicates that a social path exists from the firstuser to the second user; and a component that recommends to the firstuser the second user as a potential contact based on the existence ofthe social path; and a processor for executing the computer-executableinstructions stored in the memory.
 29. The computer system of claim 28wherein the component that indicates that a social path exists indexesthe contact paths of the first user and the second user to facilitateidentifying users common to contact paths of the first user and contactpaths of the second user.
 30. The computer system of claim 28 whereinthe component that identifies contact paths for a user traverses asocial network formed by the contact lists starting at the contact listof the user.
 31. The computer system of claim 28 wherein the componentthat identifies contact paths identifies contact paths of a maximumlength.
 32. The computer system of claim 31 wherein the maximum contactpath is three.
 33. The computer system of claim 31 wherein the maximumsocial path length is six.
 34. The computer system of claim 28 whereinthe component that indicates that a social path exists identifies allsocial paths between the first user and the second user that are throughthe same other user.
 35. A computer-readable storage medium containinginstructions for controlling a computing system to identify potentialcontacts for a target user, by a method comprising: providing aplurality of contact lists of users, each contact list identifyingcontacts of that user; identifying from a contact list of the targetuser contact paths from the target user to other users that are within amaximum contact path length, a contact path being a directed path ofcontacts such that each contact in the directed path is in the contactlist of the prior contact in the directed path; ranking users on theidentified contact paths; filtering out users on the identified contactpaths who do not satisfy a recommendation criterion; and storing anindication of the ranking of the non-filtered-out users as anidentification of potential contacts.
 36. The computer-readable storagemedium of claim 35 wherein the identifying of contact paths traverses asocial network formed by the contact lists of users starting at thecontact list of the target user.
 37. The computer-readable storagemedium of claim 35 wherein the ranking of users generates arecommendation score for a user by aggregating a path score for eachcontact path from the target user to the user.
 38. The computer-readablestorage medium of claim 37 where the path score of a contact pathdecreases the length of the contact path increases.
 39. Thecomputer-readable storage medium of claim 35 wherein the recommendationcriterion is that a user is not in the contact list of the target userand that there are multiple contact paths between the target user andthe user.
 40. The computer-readable storage medium of claim 35including, when there is a contact path from the target user to commonuser and a contact path from another user to that same common user,indicates that a social path exists from the target user to the otheruser and factoring in social paths when ranking user.